Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, does the type of ecological interaction, such as mutualism or competition, change the average effect of a mutation (i.e., the mean of the distribution of fitness effects)? Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, we discovered that in the mutualism S. enterica obtained vitamins and more amino acids than previously known. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, it would be useful to know if the type of ecological interaction, such as mutualism or competition, changes the average effect of a mutation (i.e., the mean of the distribution of fitness effects). Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, the fitness effects of several knockouts in the mutualistic communities were surprising. We discovered that S. enterica is obtaining a different source of carbon and more vitamins and amino acids than we had expected. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Disentangling a metabolic cross-feeding in a halophilic archaea-bacteria consortium.

Microbial syntrophy, a cooperative metabolic interaction among prokaryotes, serves a critical role in shaping communities, due to the auxotrophic nature of many microorganisms. Syntrophy played a key role in the evolution of life, including the hypothesized origin of eukaryotes. In a recent exploration of the microbial mats within the exceptional and uniquely extreme Cuatro Cienegas Basin (CCB), a halophilic isolate, designated as AD140, emerged as a standout due to its distinct growth pattern. Subsequent genome sequencing revealed AD140 to be a co-culture of a halophilic archaeon from the Halorubrum genus and a marine halophilic bacterium, Marinococcus luteus, both occupying the same ecological niche. This intriguing coexistence hints at an early-stage symbiotic relationship that thrives on adaptability. By delving into their metabolic interdependence through genomic analysis, this study aims to uncover shared characteristics that enhance their symbiotic association, offering insights into the evolution of halophilic microorganisms and their remarkable adaptations to high-salinity environments.

Differential retention contributes to racial/ethnic disparity in U.S. academia.

Several racial and ethnic identities are widely understood to be under-represented within academia, however, actual quantification of this under-representation is surprisingly limited. Challenges include data availability, demographic inertia and identifying comparison points. We use de-aggregated data from the U.S. National Science Foundation to construct a null model of ethnic and racial representation in one of the world's largest academic communities. Making comparisons between our model and actual representation in academia allows us to measure the effects of retention (while controlling for recruitment) at different academic stages. We find that, regardless of recruitment, failed retention contributes to mis-representation across academia and that the stages responsible for the largest disparities differ by race and ethnicity: for Black and Hispanic scholars this occurs at the transition from graduate student to postdoctoral researcher whereas for Native American/Alaskan Native and Native Hawaiian/Pacific Islander scholars this occurs at transitions to and within faculty stages. Even for Asian and Asian-Americans, often perceived as well represented, circumstances are complex and depend on choice of baseline. Our findings demonstrate that while recruitment continues to be important, retention is also a pervasive barrier to proportional representation. Therefore, strategies to reduce mis-representation in academia must address retention. Although our model does not directly suggest specific strategies, our framework could be used to project how representation in academia might change in the long-term under different scenarios.

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

Phage cocktail strategies for the suppression of a pathogen in a cross-feeding coculture.

Cocktail combinations of bacteria-infecting viruses (bacteriophages) can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phages targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually exclusive mechanisms could explain these results: (i) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phages, and (ii) S. enterica set the growth rate of the coculture, and therefore, targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed.

Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica.

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono- and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.

Increasing growth rate slows adaptation when genotypes compete for diffusing resources.

The rate at which a species responds to natural selection is a central predictor of the species' ability to adapt to environmental change. It is well-known that spatially-structured environments slow the rate of adaptation due to increased intra-genotype competition. Here, we show that this effect magnifies over time as a species becomes better adapted and grows faster. Using a reaction-diffusion model, we demonstrate that growth rates are inextricably coupled with effective spatial scales, such that higher growth rates cause more localized competition. This has two effects: selection requires more generations for beneficial mutations to fix, and spatially-caused genetic drift increases. Together, these effects diminish the value of additional growth rate mutations in structured environments.

Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community.

Bacteriophage shape the composition and function of microbial communities. Yet it remains difficult to predict the effect of phage on microbial interactions. Specifically, little is known about how phage influence mutualisms in networks of cross-feeding bacteria. We mathematically modeled the impacts of phage in a synthetic microbial community in which Escherichia coli and Salmonella enterica exchange essential metabolites. In this model, independent phage attack of either species was sufficient to temporarily inhibit both members of the mutualism; however, the evolution of phage resistance facilitated yields similar to those observed in the absence of phage. In laboratory experiments, attack of S. enterica with P22vir phage followed these modeling expectations of delayed community growth with little change in the final yield of bacteria. In contrast, when E. coli was attacked with T7 phage, S. enterica, the nonhost species, reached higher yields compared with no-phage controls. T7 infection increased nonhost yield by releasing consumable cell debris, and by driving evolution of partially resistant E. coli that secreted more carbon. Our results demonstrate that phage can have extensive indirect effects in microbial communities, that the nature of these indirect effects depends on metabolic and evolutionary mechanisms, and that knowing the degree of evolved resistance leads to qualitatively different predictions of bacterial community dynamics in response to phage attack.

Genomewide Assessment of Mycobacterium tuberculosis Conditionally Essential Metabolic Pathways.

A better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditionally essential genes by Tn-seq, we used media with different nutrient compositions. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolite availability. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex, including some existing antitubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions, highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis IMPORTANCE Mycobacterium tuberculosis causes 10 million cases of tuberculosis (TB), resulting in over 1 million deaths each year. TB therapy is challenging because it requires a minimum of 6 months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug-resistant M. tuberculosis necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genomewide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential antitubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

The Power of Metabolism for Predicting Microbial Community Dynamics.

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.

A shared limiting resource leads to competitive exclusion in a cross-feeding system.

Species interactions and coexistence are often dependent upon environmental conditions. When two cross-feeding bacteria exchange essential nutrients, the addition of a cross-fed nutrient to the environment can release one species from its dependence on the other. Previous studies suggest that continued coexistence depends on relative growth rates: coexistence is maintained if the slower-growing species is released from its dependence on the other, but if the faster-growing species is released, the slower-growing species will be lost (a hypothesis that we call 'feed the faster grower' or FFG). Using invasion-from-rare experiments with two reciprocally cross-feeding bacteria, genome-scale metabolic modelling and classical ecological models, we explored the potential for coexistence when one cross-feeder became independent. We found that whether nutrient addition shifted an interaction from mutualism to commensalism or parasitism depended on whether the nutrient that limited total growth was required by one or both species. Parasitism resulted when both species required the growth-limiting resource. Importantly, coexistence was only lost when the interaction became parasitism, and the obligate species had a slower growth rate. Under these restricted conditions, the FFG hypothesis applied. Our results contribute to a mechanistic understanding of how resources can be manipulated to alter interactions and coexistence in microbial communities.

Evolution of bidirectional costly mutualism from byproduct consumption.

Mutualisms are essential for life, yet it is unclear how they arise. A two-stage process has been proposed for the evolution of mutualisms that involve exchanges of two costly resources. First, costly provisioning by one species may be selected for if that species gains a benefit from costless byproducts generated by a second species, and cooperators get disproportionate access to byproducts. Selection could then drive the second species to provide costly resources in return. Previously, a synthetic consortium evolved the first stage of this scenario: Salmonella enterica evolved costly production of methionine in exchange for costless carbon byproducts generated by an auxotrophic Escherichia coli Growth on agar plates localized the benefits of cooperation around methionine-secreting S. enterica Here, we report that further evolution of these partners on plates led to hypercooperative E. coli that secrete the sugar galactose. Sugar secretion arose repeatedly across replicate communities and is costly to E. coli producers, but enhances the growth of S. enterica The tradeoff between individual costs and group benefits led to maintenance of both cooperative and efficient E. coli genotypes in this spatially structured environment. This study provides an experimental example of de novo, bidirectional costly mutualism evolving from byproduct consumption. The results validate the plausibility of costly cooperation emerging from initially costless exchange, a scenario widely used to explain the origin of the mutualistic species interactions that are central to life on Earth.

The spatial and metabolic basis of colony size variation.

Spatial structure impacts microbial growth and interactions, with ecological and evolutionary consequences. It is therefore important to quantitatively understand how spatial proximity affects interactions in different environments. We tested how proximity influences colony size when either Escherichia coli or Salmonella enterica are grown on various carbon sources. The importance of colony location changed with species and carbon source. Spatially explicit, genome-scale metabolic modeling recapitulated observed colony size variation. Competitors that determine territory size, according to Voronoi diagrams, were the most important drivers of variation in colony size. However, the relative importance of different competitors changed through time. Further, the effect of location increased when colonies took up resources quickly relative to the diffusion of limiting resources. These analyses made it apparent that the importance of location was smaller than expected for experiments with S. enterica growing on glucose. The accumulation of toxic byproducts appeared to limit the growth of large colonies and reduced variation in colony size. Our work provides an experimentally and theoretically grounded understanding of how location interacts with metabolism and diffusion to influence microbial interactions.

Engineering species-like barriers to sexual reproduction.

Controlling the exchange of genetic information between sexually reproducing populations has applications in agriculture, eradication of disease vectors, control of invasive species, and the safe study of emerging biotechnology applications. Here we introduce an approach to engineer a genetic barrier to sexual reproduction between otherwise compatible populations. Programmable transcription factors drive lethal gene expression in hybrid offspring following undesired mating events. As a proof of concept, we target the ACT1 promoter of the model organism Saccharomyces cerevisiae using a dCas9-based transcriptional activator. Lethal overexpression of actin results from mating this engineered strain with a strain containing the wild-type ACT1 promoter.Genetic isolation of a genetically modified organism represents a useful strategy for biocontainment. Here the authors use dCas9-VP64-driven gene expression to construct a 'species-like' barrier to reproduction between two otherwise compatible populations.

A noncatalytic function of the topoisomerase II CTD in Aurora B recruitment to inner centromeres during mitosis.

Faithful chromosome segregation depends on the precise timing of chromatid separation, which is enforced by checkpoint signals generated at kinetochores. Here, we provide evidence that the C-terminal domain (CTD) of DNA topoisomerase IIα (Topo II) provides a novel function at inner centromeres of kinetochores in mitosis. We find that the yeast CTD is required for recruitment of the tension checkpoint kinase Ipl1/Aurora B to inner centromeres in metaphase but is not required in interphase. Conserved CTD SUMOylation sites are required for Ipl1 recruitment. This inner-centromere CTD function is distinct from the catalytic activity of Topo II. Genetic and biochemical evidence suggests that Topo II recruits Ipl1 via the Haspin-histone H3 threonine 3 phosphorylation pathway. Finally, Topo II and Sgo1 are equally important for Ipl1 recruitment to inner centromeres. This indicates H3 T3-Phos/H2A T120-Phos is a universal epigenetic signature that defines the eukaryotic inner centromere and provides the binding site for Ipl1/Aurora B.

Pericentromere tension is self-regulated by spindle structure in metaphase.

During cell division, a mitotic spindle is built by the cell and acts to align and stretch duplicated sister chromosomes before their ultimate segregation into daughter cells. Stretching of the pericentromeric chromatin during metaphase is thought to generate a tension-based signal that promotes proper chromosome segregation. However, it is not known whether the mitotic spindle actively maintains a set point tension magnitude for properly attached sister chromosomes to facilitate robust mechanochemical checkpoint signaling. By imaging and tracking the thermal movements of pericentromeric fluorescent markers in Saccharomyces cerevisiae, we measured pericentromere stiffness and then used the stiffness measurements to quantitatively evaluate the tension generated by pericentromere stretch during metaphase in wild-type cells and in mutants with disrupted chromosome structure. We found that pericentromere tension in yeast is substantial (4-6 pN) and is tightly self-regulated by the mitotic spindle: through adjustments in spindle structure, the cell maintains wild-type tension magnitudes even when pericentromere stiffness is disrupted.

Monopolin recruits condensin to organize centromere DNA and repetitive DNA sequences.

The establishment and maintenance of higher-order structure at centromeres is essential for accurate chromosome segregation. The monopolin complex is thought to cross-link multiple kinetochore complexes to prevent merotelic attachments that result in chromosome missegregation. This model is based on structural analysis and the requirement that monopolin execute mitotic and meiotic chromosome segregation in Schizosaccharomyces pombe, which has more than one kinetochore-microtubule attachment/centromere, and co-orient sister chromatids in meiosis I in Saccharomyces cerevisiae. Recent data from S. pombe suggest an alternative possibility: that the recruitment of condensin is the primary function of monopolin. Here we test these models using the yeast Candida albicans. C. albicans cells lacking monopolin exhibit defects in chromosome segregation, increased distance between centromeres, and decreased stability of several types of repeat DNA. Of note, changing kinetochore-microtubule copy number from one to more than one kinetochore-microtubule/centromere does not alter the requirement for monopolin. Furthermore, monopolin recruits condensin to C. albicans centromeres, and overexpression of condensin suppresses chromosome segregation defects in strains lacking monopolin. We propose that the key function of monopolin is to recruit condensin in order to promote the assembly of higher-order structure at centromere and repetitive DNA.

Analysis and Modeling of Chromosome Congression During Mitosis in the Chemotherapy Drug Cisplatin.

The chemotherapy drug Cisplatin (cis-diamminedichloroplatinum(II)) induces crosslinks within and between DNA strands, and between DNA and nearby proteins. Therefore, Cisplatin-treated cells which progress into cell division may do so with altered chromosome mechanical properties. This could have important consequences for the successful completion of mitosis. Using Total Internal Reflection Fluorescence (TIRF) microscopy of live Cisplatin-treated Saccharomyces cerevisiae cells, we found that metaphase mitotic spindles have disorganized kinetochores relative to untreated cells, and also that there is increased variability in the chromosome stretching distance between sister centromeres. This suggests that chromosome stiffness may become more variable after Cisplatin treatment. We explored the effect of variable chromosome stiffness during mitosis using a stochastic model in which kinetochore microtubule dynamics were regulated by tension imparted by stretched sister chromosomes. Consistent with experimental results, increased variability of chromosome stiffness in the model led to disorganization of kinetochores in simulated metaphase mitotic spindles. Furthermore, the variability in simulated chromosome stretching tension was increased as chromosome stiffness became more variable. Because proper chromosome stretching tension may serve as a signal that is required for proper progression through mitosis, tension variability could act to impair this signal and thus prevent proper mitotic progression. Our results suggest a possible mitotic mode of action for the anti-cancer drug Cisplatin.